Abstract
Optimising non-functional properties of software is an important part of the implementation process. One such property is execution time, and compilers target a reduction in execution time using a variety of optimisation techniques. Compiler optimisation is not always able to produce semantically equivalent alternatives that improve execution times, even if such alternatives are known to exist. Often, this is due to the local nature of such optimisations. In this paper we present a novel framework for optimising existing software using a hybrid of evolutionary optimisation techniques. Given as input the implementation of a program or function, we use Genetic Programming to evolve a new semantically equivalent version, optimised to reduce execution time subject to a given probability distribution of inputs. We employ a co-evolved population of test cases to encourage the preservation of the program’s semantics, and exploit the original program through seeding of the population in order to focus the search. We carry out experiments to identify the important factors in maximising efficiency gains. Although in this work we have optimised execution time, other non-functional criteria could be optimised in a similar manner.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Koza, J.R.: Genetic Programming: On the Programming of Computers by Means of Natural Selection. MIT Press, Cambridge (1992)
Arcuri, A., Yao, X.: Coevolving programs and unit tests from their specification. In: IEEE International Conference on Automated Software Engineering (ASE), pp. 397–400 (2007)
Arcuri, A., Yao, X.: A novel co-evolutionary approach to automatic software bug fixing. In: IEEE Congress on Evolutionary Computation (CEC), pp. 162–168 (2008)
Langdon, W.B., Nordin, P.: Seeding genetic programming populations. In: Poli, R., Banzhaf, W., Langdon, W.B., Miller, J., Nordin, P., Fogarty, T.C. (eds.) EuroGP 2000. LNCS, vol. 1802, pp. 304–315. Springer, Heidelberg (2000)
Reformat, M., Xinwei, C., Miller, J.: On the possibilities of (pseudo-) software cloning from external interactions. Soft Computing 12(1), 29–49 (2007)
Hillis, W.D.: Co-evolving parasites improve simulated evolution as an optimization procedure. Physica D 42(1-3), 228–234 (1990)
Myers, G.: The Art of Software Testing. Wiley, New York (1979)
Binkert, N., Dreslinski, R., Hsu, L., Lim, K., Saidi, A., Reinhardt, S.: The M5 simulator: Modeling networked systems. IEEE Micro. 26(4), 52–60 (2006)
White, D.R., Clark, J., Jacob, J., Poulding, S.: Searching for Resource-Efficient Programs: Low-Power Pseudorandom Number Generators. In: GECCO 2008: Proceedings of the 10th Annual Conference on Genetic and Evolutionary Computation, pp. 1775–1782 (2008)
Zitzler, E., Laumanns, M., Thiele, L.: SPEA2: Improving the Strength Pareto Evolutionary Algorithm. Technical Report 103, Swiss Federal Institute of Technology (2001)
Arcuri, A., Lehre, P.K., Yao, X.: Theoretical runtime analyses of search algorithms on the test data generation for the triangle classification problem. In: International Workshop on Search-Based Software Testing (SBST), pp. 161–169 (2008)
McMinn, P.: Search-based software test data generation: A survey. Software Testing, Verification and Reliability 14(2), 105–156 (2004)
Miller, J., Reformat, M., Zhang, H.: Automatic test data generation using genetic algorithm and program dependence graphs. Info. and Software Technology 48(7), 586–605 (2006)
ECJ: Evolutionary computation in Java, http://www.cs.gmu.edu/~eclab/projects/ecj/
Montana, D.J.: Strongly typed GP. Evolutionary Computation 3(2), 199–230 (1995)
Montgomery, D.C.: Design and Analysis of Experiments. John Wiley & Sons, Chichester (2006)
Langdon, W.B., Poli, R.: Foundations of Genetic Programming. Springer, Heidelberg (2002)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2008 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Arcuri, A., White, D.R., Clark, J., Yao, X. (2008). Multi-objective Improvement of Software Using Co-evolution and Smart Seeding. In: Li, X., et al. Simulated Evolution and Learning. SEAL 2008. Lecture Notes in Computer Science, vol 5361. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-89694-4_7
Download citation
DOI: https://doi.org/10.1007/978-3-540-89694-4_7
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-89693-7
Online ISBN: 978-3-540-89694-4
eBook Packages: Computer ScienceComputer Science (R0)